Data Scientist
6 months ago
Responsibilities:
- Data Modeling: Develop, implement, and optimize machine learning models and algorithms to solve complex business problems and enhance decision-making processes.Data Collection and Preprocessing: Gather, clean, and preprocess data from various sources to ensure quality and relevance for model training and analysis.Feature Engineering: Identify and engineer relevant features from raw data to improve model performance and predictive accuracy.Model Evaluation and Validation: Design and execute experiments to evaluate model performance, validate results, and iterate on model improvements.Deployment and Integration: Collaborate with cross-functional teams to deploy machine learning models into production environments, ensuring scalability, efficiency, and reliability.Monitoring and Optimization: Implement monitoring systems to track model performance in real-time and optimize models based on changing data and business requirements.Documentation and Reporting: Document methodologies, findings, and insights in clear and concise reports, and communicate results to stakeholders effectively.Research and Innovation: Stay updated on the latest advancements in AI, machine learning, and data science, and explore innovative approaches to solving business challenges.
Skills and Qualifications:
Mandatory:
- Proficiency in machine learning techniques such as supervised learning, unsupervised learning, and deep learning.Strong programming skills in languages such as Python, R, or Java, with experience in libraries/frameworks like TensorFlow, PyTorch, scikit-learn, or Keras.Experience with data preprocessing, feature engineering, and model evaluation/validation techniques.Solid understanding of statistical analysis, probability theory, and mathematical concepts relevant to machine learning.Hands-on experience with data visualization tools such as Matplotlib, Seaborn, or Tableau.Excellent problem-solving skills and the ability to think critically and analytically.Effective communication and collaboration skills, with the ability to work well in a team environment.
Optional (preferred):
- Experience with cloud platforms such as AWS, Azure, or Google Cloud for model deployment and scalability.Knowledge of containerization technologies like Docker and orchestration tools like Kubernetes for managing deployment environments.Familiarity with big data technologies such as Hadoop, Spark, or Hive for processing and analyzing large datasets.Experience with natural language processing (NLP) techniques and tools for text data analysis and understanding.Understanding of software development methodologies and version control systems (e.g., Git) for collaboration and code management.Prior experience in deploying machine learning models in real-world production environments, including monitoring and maintenance.
Pay: ₹100,000.00 - ₹800,000.00 per year
**Benefits**:
- Work from home
Schedule:
- Day shift
- Monday to Friday
Supplemental pay types:
- Yearly bonus
**Experience**:
- Data science: 3 years (required)
Work Location: Remote
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